NeuroBridge
AI Enterprise SolutionsGPT & Claude

IMPERIA GROUP

Converting static educational PDFs into engaging digital lessons is a tedious, multi-hour bottleneck for educators, leaving students with passive reading and no real-time support. StudyArena automates this workflow: it ingests raw PDFs and constructs interactive micro-courses equipped with conceptual diagrams, auto-generated quizzes, and dual-level explanations. Instructors benefit from a frictionless visual builder, seamless cohort deployment, and diagnostic analytics on commonly missed questions. Concurrently, learners engage with a context-grounded AI tutor answering doubts strictly within the lesson’s scope. StudyArena replaces curriculum design friction with an autonomous, 24/7 active learning experience.

Open the demoVideoGitHub

Preliminary score

58.5/ 100Promisingaverage of 2 judges
  • Value for the user17.0 / 25

    A specific problem and a clear outcome from solving it.

  • Prototype and use of AI18.0 / 30

    A working core scenario, and what AI actually contributes to the result.

  • Quality testing10.0 / 20

    Tests, examples of failures, and comparison with the current approach.

  • Feasibility9.0 / 15

    Data requirements, running costs and a clear next step.

  • Originality4.5 / 10

    A meaningful difference from common solutions.

What the judges wrote

  • GPT-6 AstraPreliminary63/100
    Strengths
    Your PDF-to-course workflow addresses a concrete preparation bottleneck for teachers, with AI contributing directly to course generation and lesson support. Your reported 429-page conversion in 57 seconds for $0.011 gives a useful performance reference. Your disclosures about rate limits, unsupported formats and heading-recognition failures make the testing account specific and credible.
    To improve
    Your evidence would be stronger with a source PDF paired with generated lessons, diagrams and quizzes, showing the complete teacher-to-student workflow. Your tests should measure source fidelity, quiz correctness and tutor responses to out-of-scope questions across multiple PDFs, and compare instructor preparation and correction time against manual authoring. Your next-step plan should specify a teacher pilot, authentication and document-privacy safeguards, and costs for ongoing student use rather than course creation alone.
  • Claude Opus 5.5Preliminary54/100
    Strengths
    Your prototype covers a full teacher-to-student loop: PDF ingestion, generated micro-courses with Excalidraw diagrams and quizzes, cohort deployment, and a tutor limited to the lesson's scope. You give a concrete performance and cost figure (a 429-page textbook converted in 57 seconds for $0.011), which makes the cost case tangible. You also report specific failures honestly: HTTP 429 rate limits leading to a model switch and fallback, PPTX/DOCX errors, and missed headings like 'Cron (Lesson 6)' that you fixed. The stack (Go, SQLite, Poppler, Docker) is lightweight and deployable.
    To improve
    Your testing measures speed and cost but not output quality. Have teachers rate generated quizzes and explanations for accuracy, check whether the tutor stays in scope on off-topic questions, and time a manual lesson build for a direct comparison against the 'multi-hour' baseline. State a clear next step, such as a pilot with a named school or teacher group, plus the metric that would justify adoption. Your README is very thin, so add architecture notes, screenshots and example outputs as evidence that the core flow works. Also explain how you handle scanned PDFs or hallucinated quiz answers, since these are the main risks for real classrooms.

The team

  • Sahib SədiyevLead

    developer

  • Vüsal Gülməmmədov mail

    developer

  • Nəsrəddin Qaziyev

    product